Delivering Trusted Data Without Disrupting Workflows

When our buyers said, ‘It’s too much work,’ here’s what we did. Most sellers think stalled deals happen because buyers aren’t convinced of the ROI. 👉 But it's not about ROI—it’s about risk. Even when buyers agree with the problem you solve, they hesitate because of two critical questions: 1. How much work will this take to implement? →  Will it pass Infosec approval quickly? →  Does it need help from other teams? →  How steep is the learning curve? 2. How much will it disrupt our workflows? →  Does it fit into our current processes? →  Will our teams adopt the change without resistance? We faced this head-on at Factors.ai. For sales teams—one of our key personas—the value we offered required them to log into our platform regularly. For busy teams, this was a big ask. So, we asked ourselves: How can we reduce the work required to adopt Factors? Our answer: Workflow Automations. We built an embedded iPaaS solution into Factors and created integrations with 10+ tools our customers already use. Now, sales teams can access the power of Factors without ever logging in. Some automations we’ve enabled include: 💡 Automatically create companies showing intent in HubSpot, Salesforce, or Zoho. 💡 Fetch and sync contact details from Apollo into CRMs. 💡 Create follow-up tasks in Salesforce or HubSpot. 💡 Add companies to an Apollo or Dialer sequence. 💡 Sync audiences to LinkedIn Campaign Manager. 💡 Pass back conversion events to LinkedIn Conversions API. And this is just the beginning. Over the next 6 months, we’re scaling to 100+ workflow integrations. Our goal is to enable customers to adopt Factors without disrupting their workflows. Because the less effort required, the faster you see the value. What’s the biggest challenge you’ve faced when adopting new software? Let’s discuss👇 #workflowautomation #changemanagement #salesenablement #digitaltransformation

📈 This isn’t just a story about Data analytics. It’s a blueprint for telecoms and other transaction high-volume industries looking to turn complexity into clarity—and insights into impact! 🚀 For telecom companies handling terabytes of billing data each day, analytics can no longer be an afterthought. One U.S.-based broadband provider serving 20 states set out to fix a critical challenge: fragmented, delayed, and manual data processes that slowed down revenue recognition, payment tracking, and financial reporting. Billing records, revenue recognition events, payment processing logs—each minute adds gigabytes of information across critical systems like SAP S/4HANA and SAP Billing and Revenue Innovation Management (BRIM). The business imperative was clear: unlock real-time insights from this ocean of data and do it at scale, efficiently, and securely. The company’s prior approach involved custom queries, end-of-day updates, and constant monitoring—creating a bottleneck in business decision-making. The transformation began with a bold ambition: harmonize data in real time across SAP S/4HANA, BRIM, and Google BigQuery, while eliminating manual overhead and enabling a truly intelligent finance function. The solution? SAP Datasphere, acting as the heart of a modern data fabric. By implementing real-time replication flows with change data capture, and embedding a semantic layer natively in Datasphere, the team enabled curated, trusted data to flow directly into BigQuery and SAP Analytics Cloud—supporting operational and strategic decisions in near real-time. Behind the scenes, this was powered by SAP Business Technology Platform, bridging core transactional systems with cloud analytics, all while maintaining a clean core and future-ready architecture.  The IT and finance teams partnered to rethink data architecture from the ground up. Instead of traditional SLT-based replication, which had previously slowed down operational systems and lacked semantic context, the team leveraged SAP #Datasphere ’s replication flows with change data capture. 🏆 Results that Matter: • Reports that once took hours now run in real-time • 2 FTEs worth of manual effort saved weekly • 40M billing records processed monthly with precision • Foundation laid for AI-driven analytics, forecasting, and insights What made this a standout transformation wasn’t just the technology—it was the mindset. Cloud-first, agile, and committed to making data a strategic asset, not a side project! 🚀 #SAP #SAPDatasphere #Analytics #SAPBTP #S4HANA #DataStrategy #SAPAnalyticsCloud #DataArchitecture Check out the full case study in the comments section! 🚀

🔷 DataHub: Metadata that Matters In fast-growing data environments, trusting your data starts with understanding where it came from, who touched it, and how it flows across systems. That’s where DataHub earns its place. As a Senior Data Engineer, I’ve worked on projects where metadata was scattered—some in Confluence, some in spreadsheets, and some... just in someone’s head. With DataHub, we centralized metadata across: - Airflow DAGs and task-level lineage - Snowflake and Redshift schemas with column-level tracking - Kafka topics and producers/consumers for real-time observability - S3 and ADLS zones tagged with ownership, classification, and usage metrics The impact was immediate: ✅ Engineers no longer broke downstream dashboards unknowingly ✅ Analysts had self-serve discovery without relying on Slack threads ✅ Data stewards enforced naming conventions and PII tagging consistently ✅ Onboarding became faster, with lineage diagrams replacing tribal knowledge DataHub isn’t just a catalog—it’s a living map of your data landscape. It helps answer critical questions like: - “What happens if I delete this Snowflake column?” - “Is this table safe to expose to marketing?” - “Which pipelines are feeding this BI dashboard?” - “Who owns this Kafka topic and what’s the data contract?” By integrating with Git, CI/CD, and orchestration tools, it brings metadata into daily workflows instead of making it a side process. If you’re scaling your pipelines across teams, clouds, or domains—this is the kind of metadata that actually matters. #DataEngineering #DataHub #MetadataManagement #DataLineage #DataGovernance #Infodataworx #Snowflake #Airflow #Kafka #S3 #Redshift #ModernDataStack #CloudDataEngineering #DataDiscovery #SeniorDataEngineer

At Colgate, we once planned a migration using the same process we'd used three times before. It blew up on us. Why? Because the system had 20% more load, 50% more users — and we didn't find out until go-live. That's when I stopped trusting assumptions and started trusting live data. I've been doing SAP migrations for 25 years now. The players change. The destinations change. But the problem stays the same: How do you move critical business systems without breaking everything? Most companies still do assessments with spreadsheets. Think about that for a second. You're planning a $10 million project that could shut down your entire supply chain... based on static documents. What typically happens: Three system integrators bid for your assessment. They all ask for the same information. Inventory lists, performance reports, connection diagrams. They disappear for two months analyzing your data. They come back with proposals that look roughly similar. But by the time they present to management, your system has changed. You've added users, rolled out features, grown your database by 30%. The data they analyzed is stale. So you get proposals based on yesterday's reality for tomorrow's migration. I've seen Fortune 100 companies go dark for days because of this disconnect. Imagine Colgate's entire supply chain stopped. No toothpaste on Walmart shelves. No products moving through distribution. All because we trusted old data instead of live data. The solution isn't complicated: Create a live data room for your migration assessment. Not spreadsheets. Not PDFs. A connected dashboard that updates daily. When vendors need information, they access the platform. When analysts need metrics, they run reports. When timelines change, everyone sees current data. This approach cuts assessment time in half and reduces migration risk by 80%. Because the biggest risk in any migration isn't technical complexity. It's operating on outdated assumptions. Your business doesn't stand still for six months while you plan a migration. Neither should your data. Have you experienced a migration based on stale data? What happened?

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

Rethinking Data Governance: From Red Tape to Real Results Most people hear "data governance" and immediately think compliance, red tape, and slowing things down. But what if data governance was actually the accelerator? Governance should mean data we can trust, data we understand, data we can confidently use, and data that supports fast, valuable outcomes. Like brakes on a car, governance doesn’t exist to stop you; it exists so you can drive faster, safely. That’s the mindset shift: governance is enablement. Modern governance is grounded in three key principles: it’s about enabling value, speed, and understanding; not enforcing control. It only works when policies reflect how the business actually operates. And it must evolve through iteration, not perfection. This is where the concept of "Minimal Valuable Governance" comes in... do just enough to unlock value, no more, no less. That means starting with valuable problems, using data that’s already trusted and accessible, defining only what matters for the current use case, and avoiding governance debt by resisting the urge to over-document or over-engineer. >> To get started, focus on understandable data and define only the critical terms. Work closely with subject matter experts and avoid beginning with highly restricted or unfamiliar datasets. Build trust by choosing data that people already rely on and deliver context by clearly linking the data work to stakeholder goals, not abstract frameworks. #DataGovernance becomes real when it meets people where they are. Sometimes that means delivering a dashboard rather than a full-fledged data product. Prioritise outputs that drive immediate decisions or create tangible business value, whether it’s growth, cost savings, or risk mitigation. It’s also important to recognise that governance shouldn’t be a centralised compliance department necessarily. That often leads to disconnected “ivory tower” policies. Instead, embed governance directly into delivery processes, through CI/CD checks, automated lineage, pipeline validation, and close collaboration between front office and back office roles. Think about how safety is embedded in the culture of high-risk industries. Everyone, even guests, follow the rules instinctively. That’s what good data governance should feel like. It’s not a policy ... it’s just how things are done. Stop thinking of data governance as policy and start thinking of it as the foundation for trust, speed, and scale in your data ecosystem. Inspired by a Great Talk by Juan Sequeda at DataEngBytes 2025 conference in Melbourne

Too many enterprise programs still treat privacy as a policy checkbox. But privacy - done right - isn't simply about compliance. It’s about enabling confident, ethical, revenue-generating use of data. And that requires infrastructure. Most programs fail before they begin because they’re built on the wrong foundations: • Checklists, not systems. • Manual processes, not orchestration. • Role-based controls, not purpose-based permissions. The reality? If your data infrastructure can’t answer “What do I have, what can I do with it, and who’s allowed to do it?” - you’re not ready for AI. At Ethyca, we’ve spent years building the foundational control plane enterprises need to operationalize trust in AI workflows. That means: A regulatory-aware data catalog Because an “inventory” that just maps tables isn’t enough. You need context: “This field contains sensitive data regulated under GDPR Article 9,” not “email address, probably.” Automated orchestration Because when users exercise rights or data flows need to be redacted, human-in-the-loop processes implode. You need scalable, precise execution across environments - from cloud warehouses to SaaS APIs. Purpose-based access control Because role-based permissions are too blunt for the era of automated inference. What matters is: Is this dataset allowed to be used for this purpose, in this system, right now? This is what powers Fides - and it’s why we’re not just solving for privacy. We’re enabling trusted data use for growth. Without a control layer: ➡️ Your catalog is just a spreadsheet. ➡️ Your orchestration is incomplete. ➡️ Your access controls are theater. The best teams aren’t building checkbox compliance. They’re engineering for scale. Because privacy isn’t a legal problem - it’s a distributed systems engineering problem. And systems need infrastructure. We’re building that infrastructure. Is your org engineering for trusted data use - or stuck in checklist mode? Let’s talk.

DataOps: Accelerating Trustworthy Data Delivery As Enterprise Architects, we know: 👉 Moving fast with bad data is worse than moving slow. Data is now the backbone of decision-making. But speed alone won’t cut it—leaders need data that is fast, reliable, and trustworthy. This is where DataOps changes the game. Think DevOps, but for data pipelines—bringing rigor, automation, and governance to every step of delivery. What makes it different? 1️⃣ Continuous integration for data pipelines 2️⃣ Automated testing to catch issues early 3️⃣ Real-time monitoring for failures 4️⃣ Collaboration across engineering, analytics, ML, and business 5️⃣ Versioning for trust and reproducibility For Enterprise Architects, the takeaway is clear: DataOps isn’t just a technical framework—it’s a governance accelerator. It ensures the data flowing into analytics, AI, and dashboards is something your business can trust. 👉 The future of EA isn’t just designing systems. It’s ensuring those systems deliver trusted data at scale.

Frictionless Data Governance: Enabling Innovation Through Trust Organizations invest in data platforms, expecting seamless access to clean, reliable data—but instead, they encounter bottlenecks, frustration, and shadow IT. The problem? Traditional governance models focus on enforcement rather than enablement. 🚀 What if governance was built-in, not bolted on? When governance is embedded into data workflows, it removes friction, empowers teams, and ensures trust—without slowing innovation. Would love to hear your thoughts on this article on, how pushing governance left, automating metadata, and using data contracts can transform governance into an enabler, not a roadblock.